Anonymous users form a huge proportion of site users, around 90%, and pose a challenge for businesses that rely on existing user data for their decision-making. However, data from known users is only the tip of the iceberg: unrepresentative of anonymous users, who behave differently, and who likely form a much more significant part of your customer base. Let’s take a look at exactly what anonymous users are, and how first-party data can make the unknown known once more.
The ubiquity of third-party cookie tracking once meant that almost all users were essentially visible, but changes to data privacy legislation and the slow eradication of these cookies have resulted in a landscape where anonymous users are considered unknowable by many. Let’s look at what defines these users and what sets them apart from one another.
Taken literally, an anonymous user is anyone who engages with a website or app without logging in, rendering them anonymous to the platform’s owner. However, third-party cookies have historically been able to circumvent these efforts by serving targeted ads and personalized UX based on the user’s browsing history. The introduction of GDPR, CCPA, and other state privacy laws have made this far more difficult.
Today, we can talk about four types of anonymous or unknown users:
First-time visitors – even with cookies, you know nothing about a user the first time they land on your website (there are exceptions such as third-party IP databases).
Returning visitors who are not logged in will find it very difficult to connect with the profiles they have built on your sites. This is technically achievable using unique browser cookies, device fingerprinting, or IP address tracking, but due to the impending disappearance of cookies, and the multiple users who may share devices or IP addresses, each of these methods is highly prone to inaccuracy.
Visitors who have logged in, but are browsing on another device present even more problems when it comes to attributing user profile information since any cookies placed aren’t present in another. Connecting sessions across different devices is difficult for even large and sophisticated organizations, and is also going to become more difficult as data privacy becomes more stringent.
Visitors who block tracking (ad blockers, incognito): Users who choose to enable certain types of adblocking technology might prevent any kind of tracking. Users using privacy-enhanced modes such as Chrome’s incognito can be tracked with cookies for the duration of their incognito journey, but these tracking devices will delete the moment they exit privacy mode, frustrating data collection efforts.
Ad blockers currently account for 35.7% of all internet users. These users are even more difficult to predict because they can’t be tracked from site to site by cookies. Additionally, Apple devices have blocked cookie tracking since IoS 14.5, once again increasing the proportion of those impervious to third-party data collection efforts.
GDPR, CCPA, and certain other privacy legislation packages require websites to offer users the option to opt out of all tracking. Users who do so (who may account for 30-50% of traffic) won’t register on a retailer’s web analytics whatsoever.
The increasing proportion of people using ad blockers, opting out of cookies, and browsing without logging in, combine to make anonymous users by far the largest demographic online. Various estimates show that roughly 86% of internet users remain anonymous throughout their online shopping sessions. 86 is also the percentage of internet users who have taken active steps to become more anonymous online, according to the Pew Research Center.
To put this number in perspective, it is roughly equivalent to the proportion of an iceberg looming unseen beneath the water. As with icebergs, ignoring this larger, invisible element can spell disaster. However, as anonymous users do not yield the same data on purchasing history and PII as their logged-in counterparts, marketers frequently ignore them entirely. As such, anonymous users remain a vital but untapped resource for a huge number of online service providers. Let’s look at how to engage with these customers.
Even without customer profiles and the PII they deliver, there is a wealth of information we can glean from anonymous users from the moment they visit a site. Data points, such as a user’s browser, their referral source, their on-site behavior, location, device, browser extensions, whether they use incognito mode, and the weather in a user’s area, can be combined to form a detailed profile of anonymous users.
When analyzed by AI engines, this information can be used to segment customers into small clusters, all of whom behave similarly. This segmentation allows the AI to, first, make predictions relating to how the user will behave onsite, and then, to take actions based on that prediction, such as offering tailored promotions, subscriptions, or other suggestions, at a time that the user is most likely to accept.
For instance, when an anonymous user visits an airline website, they could be segmented into a cohort of those who live in Dallas, use a MacBook Pro, and browse with coupon extensions (in reality this segmentation is far more complex, and based on hundreds of data points). The AI would note that coupon extensions might suggest that the customer is a cautious spender, but the new laptop indicates that they may have high levels of disposable income. It might also note that the weather in Dallas is abysmal at the time of browsing, indicating that flights are a more valuable commodity than typically speaking. Finally, the AI would make a prediction based on these factors and many others, before offering a flight deal designed to be optimally attractive to the user, and optimally lucrative for the company.
With anonymous users far (and increasingly) outnumbering their identified counterparts, those operating online cannot afford to ignore their behavior or desires. US states are fast pushing through legislation to diminish the already curtailed reach of third-party cookies even further, while commitments by the major browsers ensure that these devices will effectively vanish by 2025.
The only way to know what is going on beneath the surface with anonymous users is to utilize first-party data. Those who provide themselves with the right tech to collect and collate this data will be able to predict their anonymous users’ behavior far more accurately than the frequently superficial information provided by the profiles of known users.
